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Inference Endpoints

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AlphaFold2-Multimer NIM provides the following endpoints:

  • protein-structure/alphafold2/multimer/predict-structure-from-sequences - Predict a protein structure given an input list of amino acid sequences.
  • protein-structure/alphafold2/multimer/predict-MSA-from-sequences - Perform a Multiple Sequence Alignment (MSA) and return the MSA and templates for AlphaFold2 inference. This endpoint is useful for batching long-running and CPU-intensive MSA runs prior to structure prediction.
  • protein-structure/alphafold2/multimer/predict-structure-from-MSA - Perform structural prediction from an input MSA and templates. This is useful when using a pre-computed or custom/external MSA.

Usage

The three endpoints follow, with requests you can run when the NIM is configured.

Predict Structure From Multiple Sequences

The predict-structure-from-sequences endpoint provides a full structural prediction pipeline, from protein sequences to a multimer protein structure. It requires at least 1 and up to 6 amino acid sequences. Tunable parameters follow:

  • sequences: An array of valid amino acid sequences. Refer to the table of amino acid codes if you are unsure if your sequence is valid.
  • databases: A list containing any of uniref90, mgnify, and small_bfd. These databases contain sequences used to generate a Multiple Sequence Alignment (MSA) that is used as input to the structural prediction neural network in AlphaFold2. In general, passing all three databases will provide the most accurate structural prediction at the cost of requiring the longest runtime.
  • algorithm: The algorithm used for Multiple Sequence Alignment. Currently, only jackhmmer is supported.
  • e_value: The sequence e-value for filtering sequences in the MSA. A smaller value is stricter as fewer sequences are included, and the MSA is less sensitive. The default is 0.0001. This value ranges from 0 to 1.
  • bit_score: The sequence bit-score to use for filtering before MSA. If passed, this is used in place of e-value for filtering. A good starting place is around 200. This value is greater than zero.
  • iterations: The number of MSA iterations to perform. In general, the default iterations=1 is sufficient and takes the least amount of time.
  • relax_prediction: Set to True to run structural relaxation after prediction. This is set to True by default and helps fix clashes in the predicted structure.

Here is an example query for sequences and a full set of databases using cURL:

curl -X 'POST' \
    -i \
    "http://localhost:8000/protein-structure/alphafold2/multimer/predict-structure-from-sequences"  \
    -H 'accept: application/json' \
    -H 'Content-Type: application/json' \
    -d '{"sequences": ["MNVIDIAIAMAI", "IAMNVIDIAAI"], "databases": ["uniref90", "mgnify", "small_bfd"]}'

Here is the same example, but this time using the Python requests module:

import requests
import json
url = "http://localhost:8000/protein-structure/alphafold2/multimer/predict-structure-from-sequences"
sequences = ["MNVIDIAIAMAI", "IAMNVIDIAAI"]  # Replace with the actual sequences you want to perform structure prediction on.
headers = {
    "content-type": "application/json"
}
data = {
    "sequences": sequences,
    "databases": ["uniref90", "mgnify", "small_bfd"]
}
response = requests.post(url, headers=headers, data=json.dumps(data))
# Check if the request was successful
if response.ok:
    print("Request succeeded:", response.json())
else:
    print("Request failed:", response.status_code, response.text)

The response is JSON and includes the predicted structure as PDB text. Refer to the PyMOL website for how to view PDB text.

Predict an MSA From Multiple Sequences

The predict-msa-from-sequences endpoint generates Multiple Sequence Alignments (MSAs) and templates used for structural prediction. This is useful if you want to batch prediction on different (CPU-intensive) nodes.

Below is an example query using cURL:

curl -X 'POST' \
    -i \
    "http://localhost:8000/protein-structure/alphafold2/multimer/predict-msa-from-sequences"  \
    -H 'accept: application/json' \
    -H 'Content-Type: application/json' \
    -d '{"sequences": ["MNVIDIAIAMAI", "IAMNVIDIAAI"], "databases": ["uniref90", "mgnify", "small_bfd"]}'

Here is the same query in Python using the requests module:

import requests
import json
url = "http://0:8000/protein-structure/alphafold2/multimer/predict-msa-from-sequences"
sequences = ["MNVIDIAIAMAI", "IAMNVIDIAAI"]  # Replace with the actual sequences you want to perform structure prediction on.
headers = {
    "content-type": "application/json"
}
data = {
    "sequences": sequences,
    "databases": ["uniref90", "mgnify", "small_bfd"]
}
response = requests.post(url, headers=headers, data=json.dumps(data))
# Check if the request was successful
if response.ok:
    print("Request succeeded:", response.json())
else:
    print("Request failed:", response.status_code, response.text)

The predict-msa-from-sequences endpoint takes the following parameters:

  • sequences: An array of valid amino acid sequences. Refer to the table of amino acid codes if you are unsure if your sequence is valid.
  • databases: A list containing any of uniref90, mgnify, and small_bfd. These databases contain sequences used to generate a Multiple Sequence Alignment (MSA) that is used as input to the structural prediction neural network in AlphaFold2. In general, passing all three databases will provide the most accurate structural prediction at the cost of requiring the longest runtime. If you must pick only one, uniref90 is considered the best choice, though it is still recommended to run with all three.
  • algorithm: The algorithm used for Multiple Sequence Alignment. Currently, only jackhmmer is supported.
  • e_value: The sequence e-value for filtering sequences in the MSA. Smaller implies stricter alignments - fewer sequences with higher probability of origin will be included, but this will also reduce the sensitivity of the MSA. The default value of 0.0001 is in general a good choice. This value ranges from 0 to 1.
  • bit_score: The sequence bit-score to use for filtering before MSA. If passed, this is used in place of e-value for filtering. A good starting place is around 200. This value is greater than zero.
  • iterations: The number of MSA iterations to perform. In general, the default iterations=1 is sufficient and takes the least amount of time.

Predict Protein Structure From MSAs

The predict-structure-from-msa endpoint takes the results of the predict-msa-from-sequences endpoint and runs structural prediction.

Do not run the MSA-to-structure prediction with cURL. The inputs include characters that need careful escaping in bash. Use the Python requests module for this endpoint.

The predict-structure-from-msa endpoint takes the following arguments:

  • sequences: An array of valid amino acid sequences. Refer to the table of amino acid codes if you are unsure if your sequence is valid.
  • alignments: The MSA results from predict-msa-from-sequences. This is an array of dictionaries, one for each input amino acid sequence, of the form {database: [database, MSA output, MSA output format]}.
  • templates: Templates from the structural database search. These use a format specific to AlphaFold2. More details of the fields are in the AlphaFold template parser.
  • relax_prediction: Set to True to run structural relaxation after prediction. This is set to True by default and helps fix clashes in the predicted structure.

Here is an example of a request to the predict-structure-from-msa endpoint using the Python requests module:

import requests
import json
url = "http://0:8000/protein-structure/alphafold2/multimer/predict-structure-from-msa"
sequences = ["STARWARSNVIDIAAAAAA"]  # Replace with the actual MSA sequences.
alignments = [{
    'uniref90': ['uniref90', '# STOCKHOLM 1.0\n\n-151285509650596177 STARWARSNVIDIAAAAAA\n#=GC RF             xxxxxxxxxxxxxxxxxxx\n//\n', 'sto'],
    'small_bfd': ['small_bfd', '# STOCKHOLM 1.0\n\n-151285509650596177 STARWARSNVIDIAAAAAA\n#=GC RF             xxxxxxxxxxxxxxxxxxx\n//\n', 'sto']
}]
templates = [
    [{'index': 1, 'name': '5X6U_E Ragulator complex protein LAMTOR3, Ragulator; Ragulator complex, scaffold, roadblock, lysosome; 2.4A {Homo sapiens}', 'aligned_cols': 10, 'sum_probs': 0.0, 'query': 'RSNVIDIAAA', 'hit_sequence': 'ASNIIDVSAA', 'indices_query': [6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 'indices_hit': [23, 24, 25, 26, 27, 28, 29, 30, 31, 32]}, {'index': 2, 'name': '5X6V_E Ragulator complex protein LAMTOR3, Ragulator; Ragulator Rag GTPase complex, scaffold; 2.02A {Homo sapiens}', 'aligned_cols': 10, 'sum_probs': 7.9, 'query': 'RSNVIDIAAA', 'hit_sequence': 'ASNIIDVSAA', 'indices_query': [6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 'indices_hit': [23, 24, 25, 26, 27, 28, 29, 30, 31, 32]}, {'index': 3, 'name': '6EHP_E Ragulator complex protein LAMTOR3, Ragulator; Scaffolding complex, Rag-GTPase, mTOR, Ragulator; 2.3A {Homo sapiens}', 'aligned_cols': 10, 'sum_probs': 0.0, 'query': 'RSNVIDIAAA', 'hit_sequence': 'ASNIIDVSAA', 'indices_query': [6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 'indices_hit': [45, 46, 47, 48, 49, 50, 51, 52, 53, 54]}, {'index': 4, 'name': '6EHR_E Ragulator complex protein LAMTOR3, Ragulator; Scaffolding complex, Rag-GTPases, mTOR, Ragulator; 2.898A {Homo sapiens}', 'aligned_cols': 10, 'sum_probs': 7.8, 'query': 'RSNVIDIAAA', 'hit_sequence': 'ASNIIDVSAA', 'indices_query': [6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 'indices_hit': [45, 46, 47, 48, 49, 50, 51, 52, 53, 54]}, {'index': 5, 'name': '6CTD_B Large-conductance mechanosensitive channel; Channel Mechanosensitive Mycobacterium tuberculosis, MEMBRANE; 5.8A {Mycobacterium tuberculosis (strain ATCC 25177 / H37Ra)}', 'aligned_cols': 11, 'sum_probs': 8.7, 'query': 'ARSNVIDIAAA', 'hit_sequence': 'ARGNIVDLAVA', 'indices_query': [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 'indices_hit': [29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]}, {'index': 6, 'name': '3HZQ_A Large-conductance mechanosensitive channel; intermediate state Mechanosensitive channel osmoregulation; 3.82A {Staphylococcus aureus subsp. aureus MW2}', 'aligned_cols': 11, 'sum_probs': 8.6, 'query': 'ARSNVIDIAAA', 'hit_sequence': 'LKGNVLDLAIA', 'indices_query': [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 'indices_hit': [29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]}, {'index': 7, 'name': '6B9X_A Ragulator complex protein LAMTOR1, Ragulator; Ragulator, Lamtor, SIGNALING PROTEIN; 1.42A {Homo sapiens}', 'aligned_cols': 12, 'sum_probs': 0.0, 'query': 'WARSNVIDIAAA', 'hit_sequence': 'KTASNIIDVSAA', 'indices_query': [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 'indices_hit': [59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70]}, {'index': 8, 'name': '4V7H_BM Ribosome; eukaryotic ribosome, 80S, RACK1 protein; HET: OMC, PSU, 5MU, 1MA, OMG, 5MC, YYG, 7MG, 2MG, H2U, M2G; 8.9A {Thermomyces lanuginosus}', 'aligned_cols': 15, 'sum_probs': 9.1, 'query': 'RWARSNVIDIAAAAA', 'hit_sequence': 'GWKAAAAAAAAAAAA', 'indices_query': [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], 'indices_hit': [139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153]}, {'index': 9, 'name': '6QKP_A Nucleoid-associated protein Lsr2; Tuberculosis, DNA organisation, Transcriptional regulator; NMR {Mycobacterium tuberculosis (strain ATCC 25618 / H37Rv)}', 'aligned_cols': 12, 'sum_probs': 9.2, 'query': 'RWARSNVIDIAA', 'hit_sequence': 'EWARRNGHNVST', 'indices_query': [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], 'indices_hit': [22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]}, {'index': 10, 'name': "1QGN_F CYSTATHIONINE GAMMA-SYNTHASE; METHIONINE BIOSYNTHESIS, PYRIDOXAL 5'-PHOSPHATE, GAMMA-FAMILY; HET: PLP; 2.9A {Nicotiana tabacum} SCOP: c.67.1.3", 'aligned_cols': 10, 'sum_probs': 0.0, 'query': 'NVIDIAAAAA', 'hit_sequence': 'KAVDAAAAAA', 'indices_query': [8, 9, 10, 11, 12, 13, 14, 15, 16, 17], 'indices_hit': [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]}, {'index': 11, 'name': '2OAR_E Large-conductance mechanosensitive channel; stretch activated ion channel mechanosensitive; 3.5A {Mycobacterium tuberculosis H37Ra} SCOP: f.16.1.1', 'aligned_cols': 11, 'sum_probs': 8.9, 'query': 'ARSNVIDIAAA', 'hit_sequence': 'ARGNIVDLAVA', 'indices_query': [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 'indices_hit': [32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42]}, {'index': 12, 'name': '5XKX_A Flavin-containing monooxygenase; Dimethylsulfoniopropionate (DMSP) lyase, LYASE; 1.5A {Acinetobacter bereziniae NIPH 3}', 'aligned_cols': 10, 'sum_probs': 8.0, 'query': 'ARWARSNVID', 'hit_sequence': 'TVWARTTAQD', 'indices_query': [2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 'indices_hit': [356, 357, 358, 359, 360, 361, 362, 363, 364, 365]}]
]
headers = {
    "content-type": "application/json"
}
data = {
    "sequences": sequences,
    "alignments": alignments,
    "templates": templates
}
response = requests.post(url, headers=headers, data=json.dumps(data))
# Check if the request was successful
if response.ok:
    print("Request succeeded:", response.json())
else:
    print("Request failed:", response.status_code, response.text)

The structural prediction module scales quadratically with sequence length. Long sequences can take several hours to predict.